Real-time monitoring of human behaviours, especially in e-Health applications, has been an active area of research in the past decades. On top of IoT-based sensing environments, anomaly detection algorithms have been proposed for the early detection of abnormalities. Gradual change procedures, commonly referred to as drift anomalies, have received much less attention in the literature because they represent a much more challenging scenario than sudden temporary changes (point anomalies). In this paper, we propose, for the first time, a fully unsupervised real-time drift detection algorithm named DynAmo, which can identify drift periods as they are happening. DynAmo comprises a dynamic clustering component to capture the overall trends of monitored behaviours and a trajectory generation component, which extracts features from the densest cluster centroids. Finally, we apply an ensemble of divergence tests on sliding reference and detection windows to detect drift periods in the behavioural sequence.

翻译：实时监测人类行为（尤其是在电子健康应用中）是过去数十年来的活跃研究领域。在基于物联网的感知环境之上，异常检测算法已被提出用于早期发现异常。渐进变化过程（通常称为漂移异常）在文献中受到的关注较少，因为相比于突发性短暂变化（点异常），它们代表更具挑战性的场景。本文首次提出一种完全无监督的实时漂移检测算法DynAmo，该算法能够在漂移发生过程中实时识别漂移时段。DynAmo包含一个动态聚类组件以捕捉监测行为的整体趋势，以及一个轨迹生成组件，可从密度最大的聚类中心提取特征。最后，我们采用一组基于滑动参考窗口与检测窗口的散度检验集成方法，来检测行为序列中的漂移时段。